Although microalgae-based processes are currently one of the most promising new technologies for the substitution of fossil fuels and chemicals, the theoretical potential of these technologies is currently limited by their low profitability, hence hindering the development of large scale plants in an economically feasible way. One of the process bottlenecks is the cultivation phase, whose operation is complicated by both the involved biological mechanisms complexity and the highly fluctuating weather conditions affecting the system. Available mathematical models describing microalgae growth and pond temperature dynamics through weather data implementation assume perfect knowledge of weather conditions, hence neglecting the inaccuracy of meteorological predictions that is expected even considering short-term forecasts. In this study a sensitivity study is first carried out to evaluate the weather variables that most impact on productivity. Then, two optimization approaches are proposed to prevent potential critical conditions (such as cell death due to too high temperatures) that may arise by using inaccurate weather forecast. The study demonstrates the reliability of the proposed methodologies and compares them in terms of productivity loss and water demand.

Microalgae growth optimization in open ponds with uncertain weather data

De-Luca, Riccardo;Barolo, Massimiliano;Bezzo, Fabrizio
2018

Abstract

Although microalgae-based processes are currently one of the most promising new technologies for the substitution of fossil fuels and chemicals, the theoretical potential of these technologies is currently limited by their low profitability, hence hindering the development of large scale plants in an economically feasible way. One of the process bottlenecks is the cultivation phase, whose operation is complicated by both the involved biological mechanisms complexity and the highly fluctuating weather conditions affecting the system. Available mathematical models describing microalgae growth and pond temperature dynamics through weather data implementation assume perfect knowledge of weather conditions, hence neglecting the inaccuracy of meteorological predictions that is expected even considering short-term forecasts. In this study a sensitivity study is first carried out to evaluate the weather variables that most impact on productivity. Then, two optimization approaches are proposed to prevent potential critical conditions (such as cell death due to too high temperatures) that may arise by using inaccurate weather forecast. The study demonstrates the reliability of the proposed methodologies and compares them in terms of productivity loss and water demand.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/3274172
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